While PRESTO is intended to yield a full conditional density rather than a single point estimate, it is still possible to provide a point estimate by calculating the mean of the predicted density. In Table REF  we report performance results for predicting point estimates as measured by RMSE. Again, we summarize the results provided in [1]} and add in the results for PRESTO. In general, the methods designed to predict point estimates outperform both PRESTO and NGBoost as measured by RMSE. Nevertheless, PRESTO gets the best results on 2 out of the 9 datasets. In head-to-head with NGBoost, PRESTO gets better results on the 5 datasets containing more than 1500 datapoints, while NGBoost outperforms on the 4 smaller datasets. One possible explanation for the lack of performance on point prediction regards the nature of density prediction. Since there is a high penalty for putting a low probability on an observed value, density predictions must be more conservative and put some probability mass on the most extreme values. Consequently, these “fat tails" influence the mean, which hurts performance on the RMSE metric.
